# Technical Architecture Companion This companion describes the ReCircuit platform architecture as a research and prototype direction. It does not claim production deployment. ## Frontend Purpose: User-facing portal for intake, dashboards, reports, research, and public communication. Inputs: User forms, image uploads, asset metadata, organization context, workflow selections. Outputs: Asset records, intake confirmations, dashboards, evidence views, downloadable reports. Current status: Public website live. Research portal generated in v2.0. Product UI remains prototype-stage. Future work: Role-based dashboard, upload experience, report preview, accessibility hardening, offline collection mode. ## Backend Purpose: Application services for asset records, workflow state, authorization, audit logging, and integrations. Inputs: Frontend requests, API calls, processor updates, model outputs, file metadata. Outputs: Validated records, workflow events, audit logs, signed report artifacts, integration responses. Current status: Architecture defined. No production backend deployment claimed. Future work: Event-driven services, tenant model, RBAC, immutable audit trail, integration adapters. ## AI Layer Purpose: Decision-support layer for classification, condition analysis, forecasting, recommendation, and text assistance. Inputs: Images, asset metadata, historical records, price signals, route data, policy references. Outputs: Predictions, confidence scores, recommended pathway, explanation notes, exception flags. Current status: Concept and prototype direction. No production model metrics claimed. Future work: Dataset design, baseline models, human review loop, evaluation harness, model cards. ## Computer Vision Purpose: Analyze device photos and component images for visible condition and category signals. Inputs: Device images, component photos, labels, damage views, battery and PCB images. Outputs: Category, condition flags, visible component hints, safety warnings, confidence score. Current status: Research and prototype design. Future work: Image capture protocol, labeled dataset, active learning, false-negative review for safety- critical classes. ## Forecasting Purpose: Forecast collection volume, route demand, downstream capacity, and market movement. Inputs: Time series intake, geography, partner capacity, seasonality, price history. Outputs: Demand forecasts, capacity warnings, pickup windows, planning signals. Current status: Proposed future capability. Future work: Synthetic planning model, pilot data capture, calibration against real operations. ## Pricing Engine Purpose: Compare economic pathways for resale, repair, parts harvesting, recycling, or destruction. Inputs: Asset category, condition, component signals, market data, processing costs, logistics costs. Outputs: Pathway economics, value range, uncertainty, recommended handling route. Current status: Architecture concept only. Future work: Market source governance, price confidence bands, override logs, regional constraints. ## Carbon Intelligence Purpose: Estimate emissions implications of alternative recovery pathways with visible assumptions. Inputs: Asset weight, material class, route, recovery pathway, emissions factors, evidence quality. Outputs: Estimated carbon impact, factor provenance, uncertainty band, methodology note. Current status: Research-stage methodology. Future work: Factor registry, methodology review, uncertainty scoring, audit export. ## ESG Engine Purpose: Prepare evidence packets for Scope 3, diversion, chain-of-custody, and recovery reporting. Inputs: Asset events, custody updates, processor records, carbon estimates, report templates. Outputs: Evidence packet, status flags, report export, data gaps, confidence labels. Current status: Report architecture defined. No audited reports claimed. Future work: Report templates aligned to customer needs, assurance workflow, API export. ## API Layer Purpose: Allow organizations and processors to exchange asset, workflow, and report data. Inputs: External system requests, webhooks, batch imports, processor confirmations. Outputs: Asset status, event updates, report data, integration notifications. Current status: Future capability. Future work: REST/GraphQL schema, webhooks, OAuth, rate limits, audit logging. ## Cloud Infrastructure Purpose: Secure, scalable runtime for data storage, models, file handling, and observability. Inputs: Application traffic, model jobs, uploaded files, logs, metrics. Outputs: Available services, monitored jobs, secure storage, backups, deployment telemetry. Current status: Not deployed as production infrastructure. Future work: IaC, secrets management, encrypted storage, monitoring, cost controls. ## Data Pipeline Purpose: Move asset evidence from intake through model processing, decisioning, reporting, and analytics. Inputs: Raw uploads, validation events, model outputs, human decisions, downstream confirmations. Outputs: Curated records, feature tables, event log, analytics views, exportable evidence. Current status: Architecture complete at concept level. Future work: Schema registry, validation rules, event bus, lineage tracking, data retention policy.